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Methodology
← Optimization & Theory
Machine Learning
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Optimization & Theory
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Theory
4950 directly classified papers
Papers per year
2000: 1
2001: 2
2002: 3
2003: 3
2004: 9
2005: 4
2006: 32
2007: 25
2008: 31
2009: 25
2010: 37
2011: 37
2012: 45
2013: 76
2014: 66
2015: 72
2016: 102
2017: 156
2018: 246
2019: 353
2020: 447
2021: 567
2022: 646
2023: 741
2024: 670
2025: 426
2026: 128
Papers
Needle In A Multimodal Haystack
NIPS 2024
Symbolic Reasoning Methods for AI Planning
AAAI 2024
Graph neural networks and non-commuting operators
NIPS 2024
Listen, Repeat, Decide: Investigating Pronunciation Variation in Spoken Word Recognition among Russian Speakers
COLING 2024
A Survey of Learning Criteria Going beyond the Usual Risk (Abstract Reprint)
AAAI 2024
Neural Model Checking
NIPS 2024
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness
NIPS 2024
Principles for AI-Assisted Social Influence and Their Application to Social Mediation
EMNLP 2024
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory
NIPS 2024
Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View
NIPS 2024
It’s Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning
ACL 2024
Schroedinger’s Threshold: When the AUC Doesn’t Predict Accuracy
COLING 2024
hinoki at SemEval-2024 Task 7: Numeral-Aware Headline Generation (English)
NAACL 2024
Updates on the Complexity of SHAP Scores
IJCAI 2024
Compact Proofs of Model Performance via Mechanistic Interpretability
NIPS 2024
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
NIPS 2024
Building Expressive and Tractable Probabilistic Generative Models: A Review
IJCAI 2024
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
IJCAI 2024
What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
NIPS 2024
Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations
IJCAI 2024
A Logic for Reasoning about Aggregate-Combine Graph Neural Networks
IJCAI 2024
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
NIPS 2024
Novelty vs. Potential Heuristics: A Comparison of Hardness Measures for Satisficing Planning
AAAI 2024
Keyphrase Generation: Lessons from a Reproducibility Study
COLING 2024
Cascade of phase transitions in the training of energy-based models
NIPS 2024
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